Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

Background & Aims: Liver involvement in sarcoidosis may occur in up to 60% of all patients. As many patients experience only minor symptoms, a high number of undiagnosed cases must be assumed. In order to successfully identify patients with hepatic sarcoidosis, a throughout characterization of these patients and their course of disease is necessary. Methods: We collected 40 patients from four German centers to evaluate current treatment standards and course of disease. All of our patients underwent liver biopsy with histologically proven granulomatous hepatitis. Results: Detailed characterization of our patients showed an overall benign course of disease. Treatment was very diverse with glucocorticoids for 1 year in 55% (22/40), 5-10 years in 18% (7/40), and permanently in 18% (7/40). Other treatments included disease-modifying anti-rheumatic drugs (DMARDs), the conventional non-biological type in 53% of all patients (of these 81% received azathioprine, 46% metotrexate, 10% hydroxychloroquine, 10% mycophenolate mofetil and 10% cyclophosphamide and biologicals in 8%. Despite these very diverse treatments, patients generally showed slow progression of the disease. Two patients died. None of our patients received a liver transplantation. Conclusions: Patients received diverse treatments and generally showed slow progression of the disease. Based on our experience, we proposed a diagnostic work up and surveillance strategy as a basis for future, prospective register studies.

Background: Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications. Methods: We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures. Results: Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively. Conclusion: Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.

No abstract

No abstract

Introduction: Hepatocellular carcinoma (HCC) may occur with several simultaneous tumor foci in the liver (multifocal HCC). Molecular biology indicated that the larger the distance between two tumor nodules, the more those two nodules differed in their genetic composition. Therefore, we explored whether the overall survival of patients with HCC depends on the mutual distance of the HCC nodules. Methods: In a retrospective study of 92 patients, CT/MRI images and survival data of the patients were collected. Based on the CT or MRI images at the time of diagnosis, the size of each tumor, the distance between the centers (center distance) and adjacent edges (edge distance) of the tumor nodules were measured respectively. These data, combined with the number of tumor nodules and clinical characteristics, were compared with the patient's overall survival data. Results: As expected, the average tumor diameter was significantly associated with patient survival in univariate cox regression analysis (p=0.00028, HR=1.2). However, in multivariate analysis, the average center distance (p=0.036, HR=1.18) and average edge distance (p=0.033, HR=0.84) were also significantly associated with survival. Discussion/Conclusion: Thus, not only the size of multiple HCC lesions but also their distance is important for the prognosis of patients with HCC. This may be of particular interest in patients with two nodules and BCLC B and C stages for the selection of therapeutic modalities and/or procedures.

No abstract

No abstract